Add config file validation entrypoint for downscaling

fme/downscaling/validate_config.py

@@ -0,0 +1,62 @@
+import argparse
+from typing import TypeVar
+
+import dacite
+import yaml
+
+from fme.core.distributed import Distributed
+
+from .evaluator import EvaluatorConfig
+from .inference import InferenceConfig
+from .predict import DownscalerConfig
+from .train import TrainerConfig
+
+T = TypeVar("T")
+
+CONFIG_CHOICES = ["train", "inference", "evaluator", "predict"]
+
+CONFIG_CLASSES: dict[str, type] = {
+    "train": TrainerConfig,
+    "inference": InferenceConfig,
+    "evaluator": EvaluatorConfig,
+    "predict": DownscalerConfig,
+}
+
+
+def validate_config(config_dict: dict, data_class: type[T]) -> T:
+    return dacite.from_dict(
+        data_class=data_class,
+        data=config_dict,
+        config=dacite.Config(strict=True),
+    )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Validate a downscaling entrypoint config from a YAML file."
+    )
+    parser.add_argument(
+        "config_type",
+        type=str,
+        help=(
+            "Kind of configuration to validate. " f"Expected one of: {CONFIG_CHOICES}."
+        ),
+    )
+    parser.add_argument(
+        "config_file",
+        type=str,
+        help="Path to the configuration file (YAML).",
+    )
+    args = parser.parse_args()
+
+    if args.config_type not in CONFIG_CLASSES:
+        raise ValueError(
+            f"Unrecognized config type: '{args.config_type}'. "
+            f"Expected one of: {CONFIG_CHOICES}."
+        )
+
+    with open(args.config_file) as f:
+        config_dict = yaml.safe_load(f)
+    with Distributed.context():
+        validate_config(config_dict, CONFIG_CLASSES[args.config_type])
+    print("Configuration is valid.")